To minimize the cost of the guessed label \(y^{\prime }\)  generated by the MLP,
we provide an initial data set having \(N\)  elements,
\(\lbrace \mathbf {x}_n,y_n\rbrace \) , where \(0<n<N\) , for use in a training
protocol. This provision for training is characteristic of
supervised learning.[1]} In this learning
procedure, each input vector \(\mathbf {x}_n\)  is accompanied by a
corresponding true classification label \(y_n\) . This label provides the
reference for a cost function \(C(y_n^{\prime },y_n)\)  which measures the
distance between the current MLP output classification label \(y_n^{\prime }\) 
and the true classification label \(y_n\) .[1]} For our
specific purposes,xs we define \(C(y_n^{\prime },y_n)\)  as
\(C(y_n^{\prime },y_n) = \frac{1}{N}\sum _n^N |y_n^{\prime } - y_n |.\) 
